Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866914156785958912 |
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| author | Decker, Thomas Tresp, Volker Buettner, Florian |
| author_facet | Decker, Thomas Tresp, Volker Buettner, Florian |
| contents | Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unknown model behavior under the specific perturbations used. This paper investigates the relationship between uncertainty calibration - the alignment of model confidence with actual accuracy - and perturbation-based explanations. We show that models systematically produce unreliable probability estimates when subjected to explainability-specific perturbations and theoretically prove that this directly undermines global and local explanation quality. To address this, we introduce ReCalX, a novel approach to recalibrate models for improved explanations while preserving their original predictions. Empirical evaluations across diverse models and datasets demonstrate that ReCalX consistently reduces perturbation-specific miscalibration most effectively while enhancing explanation robustness and the identification of globally important input features. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_10439 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration Decker, Thomas Tresp, Volker Buettner, Florian Machine Learning Artificial Intelligence Perturbation-based explanations are widely utilized to enhance the transparency of machine-learning models in practice. However, their reliability is often compromised by the unknown model behavior under the specific perturbations used. This paper investigates the relationship between uncertainty calibration - the alignment of model confidence with actual accuracy - and perturbation-based explanations. We show that models systematically produce unreliable probability estimates when subjected to explainability-specific perturbations and theoretically prove that this directly undermines global and local explanation quality. To address this, we introduce ReCalX, a novel approach to recalibrate models for improved explanations while preserving their original predictions. Empirical evaluations across diverse models and datasets demonstrate that ReCalX consistently reduces perturbation-specific miscalibration most effectively while enhancing explanation robustness and the identification of globally important input features. |
| title | Improving Perturbation-based Explanations by Understanding the Role of Uncertainty Calibration |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2511.10439 |